An Injection Optimization Decision-Making Tool Using Streamline Based Fuzzy Logic Workflow

Abstract

Streamlines provide detailed information about dynamic fluid paths and connectivity between injectors and producers. Fuzzy logic can simulate human thinking and handle different categories of information including linguistic, imprecise, approximate, and overlapping to name a few. This paper presents a genuine approach for field injection optimization using a streamline-based fuzzy logic system. In a previous paper, we presented an adaptive streamline based fuzzy logic system that uses three input parameters namely injection efficiency (IE), water cut (WC), and injection loss to aquifer to assign an injector ranking index (IRI) according to injector performance. In this paper, we enhanced the streamline based fuzzy logic workflow to smartly redistribute water injection by accounting for operational constraints and number of supported producers in a pattern in addition to the IRI. The new workflow examines the low performers (i.e., low and medium IRI categories) and assigns different injection reduction factors for each injector in these categories. Then, the total amount of reduced injection is assigned to high performers (i.e., high IRI) while ensuring no operational constraint is violated, such as BHP and capacity of pumps. This approach has been tested on a large-scale DPDP simulation model. The area of interest in this field has two rows of injectors: downdip and updip. The updip injectors are the focus of the study. The results of this case study show noticeable improvements in injection efficiency for most wells in the area of interest ensuring better sweep, good pressure support, and improving cumulative oil production. We believe combining both technologies, namely streamlines and fuzzy logic, can provide reservoir engineers with a robust decision-making tool to attain a more successful field-wide water injection strategy.

A fuzzy inference system applied to value of information assessment for oil and gas industry

“…It has been used recently in very different areas and within various problem domains, such as: the assessment of water quality in rivers (Ocampo, 2008), the improvement of image expansion quality (Sakalli et al, 1999), the differential diagnosis of non-toxic thyropathy (Guo and Ling, 2008), the development of a fuzzy logic controller for a traffic junction (Pappis and Mamdani, 1997), maintenance scheduling of Smart Grid systems (Malakhov et al, 2012), the design of fire monitoring sensors in coal mines using fuzzy logic (Muduli et al, 2017), the estimation of the impact of tax legislation reforms on the tax potential (Musayev et al, 2016), pipeline risk assessment (Jamshidi et al, 2013), depression diagnosis (Chattopadhyay, 2014), river discharge prediction assessments (Jayawardena et al, 2014), geological strength index calculation and slope stability assessments (Sonmez et al, 2004), the regulation of industrial reactors (Ghasem, 2006) and the use of a fuzzy logic approach for file management and organization (Gupta, 2011). In the domain of the oil and gas industry several applications of FIS have been reported such as the streamline based fuzzy logic workflow to redistribute water injection by accounting for operational constraints and number of supported producers in a pattern (Bukhamseen et al, 2017), the identification of horizontal well placement (Popa, 2013), for estimating strength of rock using FIS (Sari, 2016), for predicting the rate of penetration in shale formations (Ahmed et al, 2019). Fuzzy logic has been used in combination with others Artificial Intelligence techniques such as Adaptative Neuro-Fuzzy Inference system (ANFIS) on practical applications, e.g.…”